DataKitchen
AUGUST 19, 2021
Below is a discussion of a data mesh implementation in the pharmaceutical space. For those embarking on the data mesh journey, it may be helpful to discuss a real-world example and the lessons learned from an actual data mesh implementation. Some data sets are used by multiple teams, but that introduces complexity.
DataKitchen
NOVEMBER 4, 2021
The requirement to integrate enormous quantities and varieties of data coupled with extreme pressure on analytics cycle time has driven the pharmaceutical industry to lead in DataOps adoption. Cloud computing has made it much easier to integrate data sets, but that’s only the beginning. Analyst-ready trusted data sets.
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DataKitchen
AUGUST 27, 2021
DataOps has become an essential methodology in pharmaceutical enterprise data organizations, especially for commercial operations. Companies that implement it well derive significant competitive advantage from their superior ability to manage and create value from data.
DataKitchen
AUGUST 9, 2021
We’ve talked about data mesh in organizational terms (see our first post, “ What is a Data Mesh? ”) and how team structure supports agility. Let’s take a look at some technical aspects of data mesh so we can work our way towards a pharmaceutical industry application example. .
Rockset
JANUARY 31, 2023
SkyHive platform Challenges with MongoDB for Analytical Queries 16 TB of raw text data from our web crawlers and other data feeds is dumped daily into our S3 data lake. That data was processed and then loaded into our analytics and serving database, MongoDB.
DataKitchen
AUGUST 3, 2021
DataOps helps the data mesh deliver greater business agility by enabling decentralized domains to work in concert. . This post (1 of 5) is the beginning of a series that explores the benefits and challenges of implementing a data mesh and reviews lessons learned from a pharmaceutical industry data mesh example.
Precisely
MARCH 27, 2023
Delivering With Focus: Becoming a Data-Driven Organization The world’s largest manufacturer of private label, over-the-counter (OTC) pharmaceuticals and healthcare products wanted to standardize and normalize its data to create a common catalog of data assets across all of its transactional and analytical systems.
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